A beginner-friendly introduction to MLOps

Should you’re trying to elevate your MLOps projects to the subsequent level, understanding its principles is an important a part of the method. In this text, we’ll offer an introduction to MLOps principles and elucidate the important thing concepts in an accessible manner. Each principle will receive a dedicated tutorial with practical examples in forthcoming articles. You may access all of the examples on my Github profile. Nevertheless, if you happen to’re recent to MLOps, I like to recommend starting with my beginner-friendly tutorial to get on top of things. So let’s dive in!

Table of contents:

· 1. Introduction
· 2. MLOps principles
· 3. Versioning
· 4. Testing
· 5. Automation
· 6. Monitoring and tracking
· 7. Reproducibility
· 8. Conclusion

My MLOps tutorials:

[I will be updating this list as I publish articles on the subject]

In a previous article, we defined MLOps as a set of techniques and practices used to design, construct, and deploy machine learning models in an efficient, optimized, and arranged manner. One in every of the important thing steps in MLOps is to ascertain a workflow and maintain it over time.

The MLOps workflow outlines the steps to follow in an effort to develop, deploy, and maintain machine learning models. It includes the business problem that describes the issue in a structured way, data engineering that involves all the information preparation and preprocessing, machine learning model engineering that involves all of the model processing from designing the model to its evaluation, and code engineering that involves serving the model. You may check with the previous tutorial if you happen to want more details.